nb/fred-gdp-wage.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## U.S. GDP vs. Wage Income\n",
    "\n",
    "### For every wage dollar paid, what is GDP output?\n",
    "\n",
    "- Each worker on average currently contributes over \n",
    "  90,000 dollars annually of goods and services valued as GDP.\n",
    "  \n",
    "- Each worker on average currently earns about \n",
    "  43,300 dollars annually (steadily up from 35,000 since the 1990's).\n",
    "  \n",
    "- So one dollar in paid wages currently yields \n",
    "  2.23 dollars of products and services -- \n",
    "  but that multiplier is not a constant historically.\n",
    "\n",
    "### What can we say about GDP growth by observing wage growth?\n",
    "\n",
    "We find the assumption of time-invariant multiplier \n",
    "gives poor results, whereas we obtain a reasonable \n",
    "regression fit (Appendix 3) by treating the multiplier as \n",
    "time-variant (workers are increasingly more productive):\n",
    "\n",
    "$\\%(G) \\approx 1.3 * \\%(m w)$\n",
    "\n",
    "In contrast, our *local numerical approximation* \n",
    "derived in the conclusion suggests using the most \n",
    "recent estimated parameters: \n",
    "\n",
    "$\\%(G) \\approx 1.9 * \\%(w)$\n",
    "\n",
    "So roughly speaking, 1.0% wage growth equates to 1.9% GDP growth \n",
    "(yet data shows real wages can decline substantially due to the economy).\n",
    "\n",
    "The abuse of notation is due to the fact that \n",
    "our observations are not in continuous-time, \n",
    "but rather in interpolated discrete-time \n",
    "and in (non-logarithmic) percentage terms.\n",
    "\n",
    "\n",
    "Short URL: https://git.io/gdpwage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Dependencies:*\n",
    "\n",
    "- Repository: https://github.com/rsvp/fecon235\n",
    "- Python: matplotlib, pandas\n",
    "     \n",
    "*CHANGE LOG*\n",
    "\n",
    "    2016-11-10  Revisit after two years. Use PREAMBLE-p6.16.0428.\n",
    "                   Update results with newly estimated parameters.\n",
    "                   This notebook should run under Python 2.7 or 3.\n",
    "                   Appendix 3 modified to reflect trend fit of multiplier.\n",
    "    2014-12-07  Update code and commentary.\n",
    "    2014-08-15  First version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from fecon235.fecon235 import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  Python 2.7.11\n",
      " ::  IPython 4.2.0\n",
      " ::  jupyter_core 4.1.0\n",
      " ::  notebook 4.1.0\n",
      " ::  matplotlib 1.5.1\n",
      " ::  numpy 1.10.4\n",
      " ::  pandas 0.18.0\n",
      " ::  pandas_datareader 0.2.1\n",
      " ::  Repository: fecon235 v5.16.1107 develop\n",
      " ::  Timestamp: 2016-11-11, 02:48:07 UTC\n",
      " ::  $pwd: /media/yaya/virt15h/virt/dbx/Dropbox/ipy/fecon235/nb\n"
     ]
    }
   ],
   "source": [
    "#  PREAMBLE-p6.16.0428 :: Settings and system details\n",
    "from __future__ import absolute_import, print_function\n",
    "system.specs()\n",
    "pwd = system.getpwd()   # present working directory as variable.\n",
    "print(\" ::  $pwd:\", pwd)\n",
    "#  If a module is modified, automatically reload it:\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "#       Use 0 to disable this feature.\n",
    "\n",
    "#  Notebook DISPLAY options:\n",
    "#      Represent pandas DataFrames as text; not HTML representation:\n",
    "import pandas as pd\n",
    "pd.set_option( 'display.notebook_repr_html', False )\n",
    "from IPython.display import HTML # useful for snippets\n",
    "#  e.g. HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')\n",
    "from IPython.display import Image \n",
    "#  e.g. Image(filename='holt-winters-equations.png', embed=True) # url= also works\n",
    "from IPython.display import YouTubeVideo\n",
    "#  e.g. YouTubeVideo('1j_HxD4iLn8', start='43', width=600, height=400)\n",
    "from IPython.core import page\n",
    "get_ipython().set_hook('show_in_pager', page.as_hook(page.display_page), 0)\n",
    "#  Or equivalently in config file: \"InteractiveShell.display_page = True\", \n",
    "#  which will display results in secondary notebook pager frame in a cell.\n",
    "\n",
    "#  Generate PLOTS inside notebook, \"inline\" generates static png:\n",
    "%matplotlib inline   \n",
    "#          \"notebook\" argument allows interactive zoom and resize."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine U.S. population statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Total US population in millions, released monthly:\n",
    "pop = get( m4pop ) / 1000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEaCAYAAAABnax5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab09698c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot( pop )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.13, 1.13, 0.09, 12, 780, '1952-01-01', '2016-12-01']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "georet( pop, 12 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives the annualized geometric growth rate of about 1.13%, \n",
    "but one might also look at fertility rates which supports the population, \n",
    "e.g. 2.1 children per female will ensure growth \n",
    "(cf. fertility rates in Japan which has been declining over the decades)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Fraction of population which works:\n",
    "emppop = get( m4emppop ) / 100.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Workers would be employed adults, which presumably exclude children \n",
    "(20% of pop) and most elderly persons (14% of pop). \n",
    "There is a dramatic drop in working% from about 64% in 2001 \n",
    "to about 59% recently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEaCAYAAADqqhd6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncRynBfL/AbfMmIIqZL4WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9821e6c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot( emppop )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Total US workers in millions:\n",
    "workers = todf( pop * emppop )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEaCAYAAAABnax5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa97808ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot( workers )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.18, 1.19, 1.16, 12, 778, '1952-01-01', '2016-10-01']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "georet( workers, 12 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     Y\n",
       "T                     \n",
       "2016-04-01  193.131888\n",
       "2016-05-01  193.247109\n",
       "2016-06-01  193.050956\n",
       "2016-07-01  193.502625\n",
       "2016-08-01  193.645905\n",
       "2016-09-01  194.116182\n",
       "2016-10-01  193.931271"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tail( workers )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Total population and the number of workers grow annually around 1.15% -- \n",
    "but the annualized volatility for workers is much larger than the \n",
    "total population (1.16% vs 0.09%). The decrease in workers due to the\n",
    "Great Recession is remarkably, and since that period there has \n",
    "been a steady increase north of 190 million workers.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine U.S. Gross Domestic Product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Deflator, scaled to 1 current dollar:\n",
    "defl = get( m4defl )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Nominal GDP in billions:\n",
    "gdp = get( m4gdpus )\n",
    "#  The release cycle is quarterly, but we resample to monthly,\n",
    "#  in order to sync with the deflator.\n",
    "#  We do NOT use m4gdpusr directly because that is in 2009 dollars.\n",
    "\n",
    "#  Real GDP in current billions:\n",
    "gdpr = todf( defl * gdp )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                       Y\n",
       "T                       \n",
       "2016-01-01  18500.467764\n",
       "2016-02-01  18546.746023\n",
       "2016-03-01  18589.942684\n",
       "2016-04-01  18594.323425\n",
       "2016-05-01  18626.808527\n",
       "2016-06-01  18667.648188\n",
       "2016-07-01  18723.722889"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tail( gdpr )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEaCAYAAAD3+OukAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXmYFNXV/z9fUcQFBE1EBRSNoOBGMMEkvtFxRVyIZkHU\nqBMx5hUXiCYR1Ig/Y+KuiL6SaNABghKCRElEQYVJYhTBGARZhIQwsigq4C4EmPP749ZAM07P2j1d\nXXU+z9PPdN26det8u6BO1Tl3kZnhOI7jpJPtCm2A4ziOUzjcCTiO46QYdwKO4zgpxp2A4zhOinEn\n4DiOk2LcCTiO46QYdwJO3pFUKemAQtvhOM7ncSeQQCT9R9LxzXzOvSUtz7K7yYNRJM2QdFEt+7tI\nekLSO5Lek/S0pK7V6vxY0luS3pf0W0k7ZOy7TNJsSeslPVxD+ztJekDSu5LWSSqvw94TJC2U9LGk\n5yXtm7FviqSPJH0YfTZIeq2O9m6LdL0r6dZq+26SNFfSRkk31NZOPX6HdpL+GNn9H0nnNFZnXXbX\n0NZ+kqZL+kTSAkknVNt/rqRl0W83SVLburQ6deNOwMkVpwJPZ9mnZjh/W+BJoCvQHpgdbQcDpN7A\nz4DjgP2ALwH/L+P4lcAvgFFZ2n8oOsdBwO7Aj7MZImkP4HHguqjuP4DfV+03s1PNrLWZtTGzNsCL\nwIRa2vsR0Bc4DDgcOEPSJRlVlgA/Bf6crY2Mtur6HR4A1gNfBL4PjJTUrTE662F3dR6L2tgduB6Y\nGJ0DSYcAvwbOI1zfz4CRdel16oGZ+SdBH2AMsBn4BPgQ+AnhP3slUAq8CawBfgR8BXgNWAvcl9HG\nhcALwH3A+8AC4Pg6zvs4cGaWfZXAAdH3U4FXgQ+ACmBYRr0dgbHAe8A64GXCzehmYBPwaaRpRD1+\nh3bRedtF2+OAmzP2Hwe8VcNxvwAerlZ2UPQ77FrPa/BD4IWM7Z0j27vWULdzpG3fWtr7O3BxxvYP\ngBdrqDcWuKEO27L+DpGdG4AvZewfDfyqMTrra3e0rwvhxr5LRtlfgEui778Efpex74DI1l1q0+uf\nuj/+JpAwzOwCwo3+dAtPmndm7O4FHAicDQwHrgWOBw4F+kn6ZkbdowhPmHsANwJZX78lbQ8cAzxb\nDxM/Bs43s92A04D/ldQ32nch0AboQHga/F/gMzO7HvgbcHmk6cp6nOdYws1tXbR9CMHhVfEasKek\ndvVoqxfBYd0UhTVek/TtWupvcy4z+xT4V1RenQuAv5rZm/VtL/peU1v1obbfoSuw0cz+ne1cUSjs\nGzW1VYPOWu2W9CdJP8uou9TMPslSv/q5lhKcwDYhP6fhuBNILtVDMAbcZGb/NbPnCG8Kj5nZGjNb\nRbjJfjmj/mozG2Fmm81sAvAG4aZdE8cAc6r9B64RM/urmc2Pvr8OjCfcsAE2EpxOVwv808w+rp/c\nrUjqCNzPtiGbXQlvH1V8SPiNWtejyY6EkMY6YG/gCmC0pIOy1K9+rqrz1XSu84FH6jh/TbbvWscx\nDWmr6nfYNdqm2v4tdptZOzN7MUtb1evXareZnWFmtzeyrc/Z5jQOdwLp4p2M758Bq6ttZ95YVlY7\ntgLYJ0u7pwJT6mOApKOi5N87kt4nhKW+EO0eC0wFxktaESUVW9Sn3Yz2vxi1cX/kvKr4mPCWUcVu\nBMf4UT2a/Qz4LyGMssnM/grMAE6W1ClKVH4kqeoGWv1cVefb5lyS/ocQ3348o2xoRtL4gVpsb7Bz\nrKWtqt+hXnbX0lb1+g2xu6Ft1WWbU0/cCSSTXEwN26Ha9r7Aqix16+0ECDHpJ4AOZtYW+A3RW0t0\ng/2FmR0CfAM4nRAugXpoisJVU4EnzKx6T5T5wBEZ2z0IbzvrqJu5VafIKLPI5uUWkrytLSR5q87V\nI8OuXQgJ2PnV2r0AmBSFUYjau8W2Jo0H1mJ79bbqS22/w2Jge0lfyth/RC3nyqbz9UbYPR84IGqj\npnNv01Zk4w6RzU4TcCeQTN4mJM4yaWgPnT0lXSFpe0nfAw6mhhu9pM5ASzN7o57t7gqsM7ONknoB\n52a0VSLpUEnbEZ78NhKS3BDeWrKONZDUGphGSFReV0OVMcAASd2i+Pf1ZIRhJLWQ1ApoQbgR7pjx\nFvJXQp5laFTvaKCE4HBq4o/AIZLOkrQjMIwQLttyw4rO1Y+6Q0FVtl8laR9JHYCrqtm+fdTedsAO\nke3Z/m9n/R0iZzSJkPvYOXpTOYPwhtYQnUvqY3cm0TFzgGGR/d8m5Kqq3pLGEXoXHR05ipuAx+sT\ngnTqoNCZaf/k/kPolldB6PVzFaF30GZgu4w6bwLHZGyPAa6Nvl9IyBGMIPSKWQSckOVcl1FHb53o\n3FW9g74NLCPEdydH5xgT7esfnesj4C3gniqbga8R8hJrgOE1nOOC6DwfZXw+BDpm1BlMcJDvA78F\ndsjYN4zQm2hzxueGjP3dCF05PyI86fatQ/PxwEJC7mU61Xr/RFr/04Bremuk/T3glmr7HqnB9gtq\naau236Ed4eb+cXSdzq527EfA0Q3QWZvdU4AhGdv7EsJsn0ZtHlfDb1YR2TAJaFvo/2tJ+Cj6cbMS\nJdnGEGKXlcBDZjYieor4PeEGswzoZ2YfRMcMBS4idH0bZGbTovKeQBnQCphiZoOj8pbROY6M/rGc\nbbX3lnDyiKQLgQFmdkw96j5F6F76TP4tcxwn19QnHLQJuMpCnPbrwGWSDgaGAM+Z2UGEJ4ChAJK6\nE15zuwF9gAckVYUiRhJuLl2BrtHAFYABwFoz60LouljVY8CJPzOij+M4RUidTsDM3jazOdH3jwmv\naR2BbxEGkhD9PTP63hcYbyHJt4zQ17yXpL2A1mY2O6o3JuOYzLYmAtsMF3fii5ndaWYbCm2H4ziN\no0GJ4SgJ2AOYCbQ3s9UQHAWwZ1StA5A5h8zKqKwDsCKjfAVbe6BsOcbMNgPvS9q9IbY5ucPMRtcn\nFOQ4TvFTbycgaVfCU/qg6I2gejIhlyvWN8dcM47jOKln+/pUiqYFmAiMNbOqSblWS2pvZqujUE/V\nQKSVQKeMwztGZdnKM49ZFXXLa2Nma2uwI5eOxnEcJzWYWY0P1/V9E3gYWGBm92aUTSZMSAahS+GT\nGeX9JbWUtD9hrppZUcjoA0m9okTxBdWOuTD6/j1CojmbkHp/hg0bVvDuV83xcZ3J+qRFZ5q0Flpn\nbdT5JhANjDkPmCfpn4Swz7XAbcAEhTneKwg9gjCzBZImEGae3AgMtK1WXMa2XUSruhWOAsZKWkLo\nU9y/Lrvqw7Jly3LRTOxxnckiLTohPVrjrLNOJ2BmfyeMoqyJE7MccwtwSw3l/yBMxFW9fAORE3Ec\nx3Gaj0RPG1FaWlpoE5oF15ks0qIT0qM1zjrrHDEcJyRZMdnrOI4TByRhTUwMFyXl5eWFNqFZcJ3J\nIi06IT1a46wz0U7AcRzHqR0PBzmO4ySc1IaDHMdxnNpJtBOIcxwul7jOZJEWnZAerXHWmWgn4DiO\n49SO5wQcx3ESjucEHMdxnBpJtBOIcxwul7jOZJEWnZAerXHWmWgn4DiOk3YeeaT2/Z4TcBzHSSjP\nPw/9+8N773lOwHEcJzVUVsJFF8F3vgMTJ9ZeN9FOIM5xuFziOpNFWnRCerQ2h85Vq+Dyy+ErX4Fd\ndoFFi+DNN+HYY2s/LtFOwHEcJ+ls2ACXXAKHHAI77wwjRsArr8CMGdCmTd3H15kTkDQKOB1YbWaH\nR2VHAL8mrBBWtXrYK9G+ocBFwCbCovTTovKebLuq2OCovCUwBjgSeA8428zezGKL5wQcx3EiKivh\n3HNh/XoYNQr22KPmek0dJ/AI0Lta2e3AMDP7MjAMuCM6UXfCCmHdgD7AA9F6wgAjgQFm1hXoKqmq\nzQHAWjPrAgyP2nYcx3HqYPhwWL4cxo/P7gDqok4nYGYvAOuqFVcCu0Xf2wIro+99gfFmtsnMlgFL\ngF6S9gJam9nsqN4Y4Mzo+7eA0dH3icAJjdBRIx5vTBauM3mkRWs+dM6eDbfcAg8/DK1aNb6dOtcY\nzsKPgamS7gIEfCMq7wC8lFFvZVS2CViRUb4iKq86ZjmAmW2W9L6k3c1sbSNtcxzHSSyVlXDddfDQ\nQ8EBHHRQ09prrBO4lBDvf0LSd4GHgZOaZsoWaoxbNYaSkpJcNRVrXGeySItOSI/WXOocOTIkfRcs\ngD33bHp7jXUCF5rZIAAzmyjpt1H5SqBTRr2OUVm28sxjVklqAbSp7S2gtLSUzp07A9C2bVt69Oix\n5QeueuXybd/2bd9O4vZnn8HNN5cwZQosWFDOggU11y8vL6esrAxgy/0yK2ZW5wfoDMzL2J4PHBt9\nPwGYHX3vDvwTaAnsD/yLrT2QZgK9CE/6U4BTovKBwAPR9/6EnEI2O6whzJgxo0H1ixXXmSzSotMs\nPVpzpfPmm8369Wv4cdG9s8b7ap1vApIeBUqAPSS9SegN9ENgRPTkvh64JLpDL5A0AVjA1q6jVX06\nL2PbLqLPROWjgLGSlgBrIkfgOI7jZLBgQegNNHNmbtv1uYMcx3FizIABsHlz6Araty8MGtTwNnw9\nAcdxnCLkySfhhRegfXv49FO49NLcnyPRTqAqUZJ0XGeySItOSI/WxuhcuxauuAIefBBuuw1eegla\ntsy9bYl2Ao7jOMXIpk1hBtCzz657Arim4jkBx3GcmGAG8+fDo4/Cyy/DtGnQokXT2/WcgOM4ToxZ\ntCgkf2+4AQ47DP7wB/jd73LjAOoi0U7A443JwnUmj7RozaZz8+bQ26d7dzj00DAVxPLl8PrrsPfe\nzWNbY0cMO47jOE3gb3+Dq68Oc/6vXRu227SBjh2b1w7PCTiO4zQzd98dBn7demtYA3i7PMdkassJ\nuBNwHMfJ4L33wqdr1/zcnKdMgYsvDiN/99039+3XRGoTw2mPNyYN15k84qS1shLGjAnLNB5/PAwb\nlru2y8vLqayEZ56B0tKQ+G0uB1AXnhNwHCf1fPwxnH9+WKz9T3+CvfaCnj3hpJPCer3HHw89ejSs\nzdWrw3q/gwfD9Onwox+FN4tHHoGjj86Pjsbg4SDHcVLNAw+EEbknnAC//vXWUbmjR4en9kMPDU5i\n8WLYYYf6tbluHZSUwBe+EBzAXnuF9o4/HrYvwKO35wQcx3Fq4G9/g/POg4kToVevz+9ftw7atYNT\nTgk38J/9rO42N2+Gk08O/f3vuQfKy6FzZ9h//1xbX388J5BwXGeySItOKKzWysrQRfOWW2p2ABAc\nAMD998Ptt8PGjXW3O2wYSHDXXeHvccdBRUV5zuzONZ4TcBwnNXz4YVif9403Qsy+ZUs455y6jzvw\nwPCZPh16985e79VXYdQoeO215hntmws8HOQ4TirYuBFOOy3E5887Lzzld+sGrVvX7/i77oIXX4TT\nTw99+3fa6fN1Tj4ZzjorP1M+N4UmhYMkjZK0WtLcauVXSFooaZ6kWzPKh0paEu07OaO8p6S5khZL\nGp5R3lLS+OiYlyTFpOOU4zhJYvx4WL8eHn44PM336lV/BwDBcUyaBBddFHr9VOfJJ2HZsjAGoJio\nT07gEWCbFyBJJcAZwGFmdhhwZ1TeDegHdAP6AA9IqvI+I4EBZtYV6Cqpqs0BwFoz6wIMB25vkqIM\n0hJbdZ3JIi06oXm1jhkT5udvbO+cvfaClSth3jy48054/vlQ/umncM01cMklwcHU1IMozte0zp/D\nzF6QtF+14kuBW81sU1Tnvaj8W4SF4jcBy6J1g3tJqgBam9nsqN4Y4ExganRM1bCMicD9TRHkOI6T\nyaxZ4eY8e3YYA9AU9tknfMaPh+9+N4wj+Ne/wujiWbNgv+p3yiKgsb2DugLHSJopaYakI6PyDsDy\njHoro7IOwIqM8hVR2TbHmNlm4H1JuzfSrm0oKSnJRTOxx3Umi7TohPxrXbIk5AEWLQohnFatctPu\nCSeEN4JNm8IawI89VrsDiPM1bWzvoO2Bdmb2NUlfBf4AHJAjm2pMXjiO4zSUX/0qhIBuuCH3bXfs\nGHIExU5jncByYBKAmc2WtFnSHoQn/8zEbseobCXQqYZyMvatktQCaGNma7OduLS0lM6dOwPQtm1b\nevToscXLVsXdqraHDx9e6/6kbFeVxcWefG379Uze9pw5cxg8eHDO2zeD664r5/HHoaKi8HqrX9vm\nOF9ZWRnAlvtlVsyszg/QGZiXsX0J8P+i712Biuh7d+CfQEtgf+BfbO2GOhPoRXjSnwKcEpUPBB6I\nvvcn5BSy2WENYcaMGQ2qX6y4zmSRFp1m+dN6zTVmhx9u9soreWm+wRT6mkb3zhrvq3WOE5D0KFAC\n7AGsJiRxxxJ6DfUANgBXm9lfovpDCT1+NgKDzGxaVH4kUAa0AqaY2aCofMeovS8Da4D+ZrYsiy1W\nl72O46SbtWvhS18Ka/Xus0+hrYkHPneQ4zipYdgwqKiAKBri4HMHJR7XmSzSohNyr3XixDAT6E03\n5bTZJhPna+pzBzmOkwgqKsJ0DVOnxmfBlmLAw0GO4xQ969eHqSBOPTWM3nW2xXMCjuMklsrKMHp3\nu+3g978vntk7mxPPCSQc15ks0qITcqP15z+Hd9+FcePi6wDifE09J+A4TtFyxx3w6KNh3p4ddyy0\nNcWJh4McxylKfvc7+OUv4emnw/KNTnY8J+A4TqL4+GM46KAwd89RRxXamvjjOYGE4zqTRVp0QuO0\nmsGgQXDiicXjAOJ8TT0n4DhOUTF+PLz8MsycWWhLkoGHgxzHKRoqK0MYaNQoOOaYQltTPKQ2HOQ4\nTrL4y1/CAu/f/GahLUkOiXYCcY7D5RLXmSzSohMarvW3vw0LvavIlp6K8zVNtBNwHCc5zJ8f5gW6\n8MJCW5IsPCfgOE7sGTcu9Ai6+2644IJCW1N81JYT8N5BjuPEkiVLws1/0SKYPRumT4fDDy+0Vcmj\nznCQpFGSVkuaW8O+qyVVSto9o2yopCWSFko6OaO8p6S5khZLGp5R3lLS+OiYlyTlbBLYOMfhconr\nTBZp0Qmf12oGY8dCnz7w9a/DmjWhF9Ds2cXtAOJ8TevzJvAIcB8wJrNQUkfgJKAio6wb0A/oRlhM\n/jlJXaIYzkhggIWF6adI6m1mUwlLUa41sy6SzgZuJ6w17DhOyrjzThg9OkwH/eCD0KlToS1KPvXK\nCUjaD/iTmR2eUfYH4CZgMnCkma2VNISwoPFtUZ2ngRsJjmK6mXWPyvsDx5rZpZKeAYaZ2cuSWgBv\nm9kXs9jhOQHHKXL++194880w38+NN8Jbb8Ett4TVwCZMgFde8UVhck3OxwlI6gssN7N51XZ1AJZn\nbK+MyjoAKzLKV0Rl2xxjZpuB9zPDS47jFD9mIaZ/771w4IFw9NGwww5hOchVq6B9+xD6efZZdwDN\nTYMTw5J2Aq4lhILyQa09gEtLS+kcTRnYtm1bevToQUlJCbA17la1PXz48Fr3J2W7qiwu9uRr269n\n8W6PGwdXX13OwQfDqFElnHgiXH99OTvtNIfrrx/Mp5/CrFnlrFsHUHh7c71d/do2x/nKysoAttwv\ns2JmdX6A/YC50fdDgbeBpcB/gI3AMmBPYAgwJOO4Z4CjgL2AhRnl/YGRmXWi7y2Ad2qxwxrCjBkz\nGlS/WHGdySJpOjdvNjvkELOpUz+/L2las1FondG9s8b7an1zAp0JOYHDatj3H6Cnma2T1B0YF934\nOwDPAl3MzCTNBK4EZgNPASPM7BlJA4FDzWxglCs408xqTAx7TsBxio/Jk0Ps/x//KL6RvkmhSeME\nJD1KeD/bQ9KbhCTuIxlVjCiEY2YLJE0AFhDeEAZm3LUvA8qAVsAUM3smKh8FjJW0BFiD9wxynESw\neTPccENY/WviRHcAcaXOxLCZnWtm+5jZjma2bzUHgJkdYGZrM7ZvMbMDzaybmU3LKP+HmR1mZl3M\nbFBG+QYz6xeVf83MluVI2zZxuCTjOpNFUnSWlcG0aVBRAX371lwnKVrrIs46fcSw4zg555134Npr\n4c9/hr33LrQ1Tm343EGO4+QUszDT5y67wP33F9oaB3zuIMdxmpHf/Q5efRViHAFxMkj0VNJxjsPl\nEteZLIpZ58iR8MMfwkMPQbt2ddcvZq0NIc46E+0EHMdpPiZNgptvhqeegl69Cm2NU188J+A4TpMJ\nI4JDIvgrXym0NU51assJuBNwHKdJLFgAxx4b1v/t3r3Q1jg1kdqF5uMch8slrjNZFJvOn/40dAdt\njAMoNq2NJc46E+0EHMfJL2PHwtKlMHBgoS1xGouHgxzHaRRLl4YE8IwZcNjnZhVz4kRqw0GO4+SH\nt96C004LcwO5AyhuEu0E4hyHyyWuM1nEXedbb8Fxx8F558GVVzatrbhrzRVx1ploJ+A4Tm6pcgDf\n/z5cf32hrXFygecEHMepF48/DlddBZdcAtddV2hrnIbgcwc5jtMkxo0LT/5jxoQxAU5yqDMcJGmU\npNWS5maU3S5poaQ5kh6X1CZj31BJS6L9J2eU95Q0V9JiScMzyltKGh8d85KknC0zHec4XC5xncki\nbjqXLoXBg+GJJ3LvAOKmNV/EWWd9cgKPAL2rlU0DDjGzHsASYChAtLxkP6Ab0Ad4QNqyntBIYICZ\ndQW6SqpqcwCw1sy6AMOB25ugx3GcHLJpU4j/X3stHHFEoa1x8kF91xjej7DG8OE17DsT+I6ZnS9p\nCGFB49uifU8DNwIVwHQz6x6V9weONbNLJT1DWLLyZUktgLfN7ItZ7PCcgOM0I9deG9YGfvpp2M67\nkRQt+c4JXAQ8Fn3vALyUsW9lVLYJWJFRviIqrzpmOYCZbZb0vqTdM5esdByn+bnnnrA28N/+5g4g\nyTTp0kq6DthoZo/VWbkBzeaqoTjH4XKJ60wWhda5YUPIAdx/Pzz/PLRvn79zFVprcxFnnY1+E5BU\nCpwKHJ9RvBLolLHdMSrLVp55zKooHNSmtreA0tJSOnfuDEDbtm3p0aMHJSUlwNYfump7zpw522xX\n35+U7SriYk++tv165n97/Xq4554SWrWCe+4p59//hk6d8ne+OXPmFPz3TuJ2eXk5ZWVlAFvul9mo\nb06gMyEncFi0fQpwF3CMma3JqNcdGAccRQjzPAt0MTOTNBO4EpgNPAWMMLNnJA0EDjWzgVGu4Ewz\n65/FDs8JOE6emDcPvvc9OOooGDUKtvcO5ImhSesJSHoUKAH2AFYDw4BrgZZAlQOYaWYDo/pDCT1+\nNgKDzGxaVH4kUAa0AqaY2aCofEdgLPDlqL3+ZrYsiy3uBBwnD3z4Yej98/Ofh0XinWSR2kVlysvL\nt7wqJRnXmSyaW+f69XDxxbDzzvDgg812WsCvaXPhI4Ydx6mRRYvg7LNhzz3DtBBO+kj0m4DjODVT\nWQl33gl33BGmg778clDO+uU5ccPfBBzHYeNG+Oc/4YMP4N57Yc2aMBBs35xN1OIUI4keAlK9y11S\ncZ3JIh86p0+HAw8MSd9bb4UvfzksDF9oB+DXtPD4m4DjJBgzePbZsADMI4+E1cA87ONk4jkBx0ko\nb7wB55wDa9fCb38LJ55YaIucQuFrDDtOynj33fDUP2BAmAraHYCTjUQ7gTjH4XKJ60wWTdX57rtw\nyimh6+dll8V78je/poUnxv88HMdpKCtWwDHHQJ8+cPPNhbbGKQY8J+A4CWHdOjj+eOjXD4YOLbQ1\nTpzwnIDjJJhNm+BPf4KvfhVOOAGGDCm0RU4xkWgnEOc4XC5xncmiPjorKuCKK+D880P//1/9Knzu\nvLO4uoD6NS08iXYCjpNE5s+H446DXXaBk08Oc/689FIIAzlOQ/GcgOMUCWZhta9f/CJ8fvSjQlvk\nFAs+d5DjxJQ1a2DuXPjGN2DHHbPX27gRrrwSXngBZs6EAw5oPhudZJPocFCc43C5xHUWH6NHw377\nwT77wI9/HKZyXrYs7MvUWVkJixeHfv8VFfD3vyfLASTpmtZGnHXW6QQkjZK0WtLcjLJ2kqZJekPS\nVEm7ZewbKmmJpIWSTs4o7ylprqTFkoZnlLeUND465iVJPqehk0jeeSc8zXfqFG78kybBp5/CnDlw\nzTXwv/8bQj5m8Pvfw3e+E+L+xxwTun5Ongxt2hRahZM06rO85P8AHwNjzOzwqOw2YI2Z3S7pGqCd\nmQ3JWGP4q4TF5J9j6xrDLwOXm9lsSVOAe81sqqRLgcOiNYbPBs7yNYadJFFREeL3f/97mMbhwguh\nffvwFlDFxo3w9a9DixbhRr9iBVx9dVjzd7fdsrftOPWhSTkBM3tB0n7Vir8FHBt9Hw2UA0OAvsB4\nM9sELJO0BOglqQJobWazo2PGAGcCU6O2hkXlE4H76yvMceLOq6/CGWeEN4BJk8ISjjWxww5huueX\nXgp/H30UvvjF5rXVSSeNzQnsaWarAczsbWDPqLwDsDyj3sqorAOwIqN8RVS2zTFmthl4X9LujbRr\nG+Ich8slrjOeTJ4cYvkjRoRwTzYHUEWbNtC7N/TpU54aB1Bs17SxxFlnrnoH5TJGU0RDXRzn86xc\nCYMHh1W7nnwyhHkcJ6401gmsltTezFZL2gt4JypfCXTKqNcxKstWnnnMKkktgDZmtjbbiUtLS+nc\nuTMAbdu2pUePHpSUlABbvW3VdlVZtv2+XVzbVWVxsaem7WnT4MEHSxg4EC6+uJwNGwDiY18ct6uI\niz352C4pKWnW85WXl1NWVgaw5X6ZjXoNFpPUGfiTmR0Wbd8GrDWz27Ikho8ihHmeZWtieCZwJTAb\neAoYYWbPSBoIHBolhvsDZ3pi2Ck21q+HUaPgpptCTP+QQwptkeNspUkTyEl6FHgR6CrpTUk/AG4F\nTpL0BnBCtI2ZLQAmAAuAKcDAjLv2ZcAoYDGwxMyeicpHAV+IksiDCQnmnFD9SSOpuM7CsnRpuOk/\n8URI/jbVAcRVZz5Ii9Y466xP76Bzs+yqca0iM7sFuKWG8n8Ah9VQvgHwWU+couTtt8P8PT/5CVx6\naaGtcZyG43MHOU4j+O9/4bHHwrTNgwb59M1OvPG5gxwnR5jB88+HJ//WrUPvn169Cm2V4zQenzso\nAbjO/PN4pZWPAAAWHklEQVTee3DjjWH6hssvD11A//rX/DiAtFxPSI/WOOtMtBNwnKZiBvfcAwcd\nFOL/l18Or70GpaXFtXiL42TDcwKOUwOLFoUlGydPDvH/3/0OunQptFWO0zh8jWHHqSf//neYuqGk\nJEz8dtVV8Le/uQNwkkuinUCc43C5xHXmhoULQ8z/hBOCM7j/fjjrLGjZMq+n/RxpuZ6QHq1x1um9\ng5zUM28eDB0aVuy6884Q73ectOA5ASfVzJoFffvCddfBaacla9Uux6nCxwk4TjVmzw4DvN54A+67\nLyze4jhpxHMCCcB1NozHHw9P/d//fpj3J24OIC3XE9KjNc46/U3ASRUTJsAVV8C0adCjR6GtcZzC\n4zkBJzX84x/Qp487ACd9+DgBJ/Vs2BB6/dx9tzsAx8kk0U4gznG4XOI66+anP4UDD4TzzsudPfki\nLdcT0qM1zjo9J+Aknl//GqZMCT2CfL4fx9mWJuUEJP0YGABUAvOAHwC7AL8H9gOWAf3M7IOo/lDg\nImATMMjMpkXlPYEyoBUwxcwGZzmf5wScBvGnP8HAgVBeDl/6UqGtcZzCkJecgKR9gCuAnmZ2OOGt\n4hzC8pDPmdlBwHRgaFS/O2EFsW5AH+ABactz2UhggJl1JSxj2buxdjnJxCys49sQ3nkHLrkExo93\nB+A42WhqTqAFsIuk7YGdgJXAt4DR0f7RwJnR977AeDPbZGbLgCVAL0l7Aa3NbHZUb0zGMU0iznG4\nXFJsOteuDTfmG26ACy+Ec88N6/L27w8jRoSbd3WuvRb23rucO+4Iid0f/AAqK7Ofwyw4gNJSOPro\nvEnJC8V2PZtCWrTGWWejnYCZrQLuAt4k3Pw/MLPngPZmtjqq8zawZ3RIB2B5RhMro7IOwIqM8hVR\nmZNAFi2CI44ISzNCmK3zpJNgzJjQffPVV+Goo0JvniomT4Zx47Yu5PKzn8HixWGQ18cff/4cZmEU\n8H/+ExaCcRynFsysUR+gLfA8sDvhjWAScB6wtlq9NdHf+4BzM8p/C3wbOBKYllH+P8DkLOc0pzhZ\nt87smmvM9tjDrKys9rqnnGI2cmT4vnSp2Z57mr300rZ1PvzQ7IILzHr3Nnv8cbMLLzRbtCjsu/tu\nsy5dzObPz7kMxylKontnjffypvQOOhFYamZrAST9EfgGsFpSezNbHYV6ql7uVwKdMo7vGJVlK6+R\n0tJSOnfuDEDbtm3p0aMHJSUlwNZXLt+O3/ZNN8HLL5dz771w3nm11x82rISzz4bOncv5yU/gZz8r\n4Wtf27Z+69ZwwQXlDBsGl19ewvnnw1e/Ws4OO8D69SW8/jpUVJTzzjvx0O/bvt2c2+Xl5ZSVlQFs\nuV9mJZt3qOsD9CL0CGoFiNC75zLgNuCaqM41wK3R9+7AP4GWwP7Av9jaO2lm1J6AKcApWc7ZIO83\nY8aMRvrN4iLuOj/7zKxdO7OKivof06+fWatWZmecYbZ5cyjLprOyMvxdscJs4UKzDRuaZm+hifv1\nzCVp0VponeTjTcDMZkmaGN3YN0Z/HwRaAxMkXQRUEHoEYWYLJE0AFkT1B0bGETmPMrZ2EX2msXY5\n8ePPf4aePWHffet/zO9/HxZ333132K6OzFVVH7MOnklynAbjcwc5eWPVKigrg0cfhauvDj16HMdp\nfnzuIKdZ2bgxDNA65BBYvjxM2VAM0zU4ThpJtBOoSpQknTjp/OyzsC7vm2+GLpojR4axAC1zsE5v\nnHTmk7TohPRojbNOnzvIyRkbN4Yb/s47h379O+xQaIscx6kLzwk4OWHpUvj2t6FjR5g4EVq1KrRF\njuNU4TkBJ29s3gyjR4epGS6+OEzY5g7AcYqHRDuBOMfhckkhdL7ySpjzp3VrePBBmDQJLr88v1M1\n+/VMHmnRGmediXYCTu6prAxr9H772/CVr8CKFfD3v8PXv15oyxzHaQyeE0g5//1v+Fuf3juffgrf\n/34YxPXnP0ObNvm1zXGc3OA5AWcbVq+G55+H3/wGDjoI9t8f7r03DOxatiz7MccdB7vsAs8+6w7A\ncZJCop1AnONwuaQhOkeMCIO4bropTMs8YgQ88QTMmgVTp4YQz09+Ai+/DJs2hWOWLg3hnlNOCVM+\n77hjfnTUhV/P5JEWrXHW6eMEUsRzz8Fdd4UbfPWVtsaNC38rKuD//g/OOSfMxXPFFWFO/ssuC1M/\nOI6TLDwnkBI++wwOPxzuuQdOP73u+pWVoe5TT4U5f77/fV+k3XGKldpyAu4EUoAZXHoprFsXZud0\nHCddpDYxHOc4XC6pTadZeKJ/4QV46KHmsykf+PVMHmnRGmednhNIMGvWwEUXhfV4p0zxHj2O43we\nDwcllM2bw8LtX/oSDB9euB49juMUnryFgyTtJukPkhZKmi/pKEntJE2T9IakqZJ2y6g/VNKSqP7J\nGeU9Jc2VtFjS8KbY5IQQ0GWXheTuffe5A3AcJztNzQncS1gOshtwBLAIGAI8Z2YHAdOBoQCSuhOW\nmuwG9AEekLb0NxkJDDCzrkBXSb2baBcQ7zhcLqmu89e/DlM5PPEEbJ+ggF9ar2eSSYvWOOts9C1C\nUhvgm2ZWCmBmm4APJH0LODaqNhooJziGvsD4qN4ySUuAXpIqgNZmNjs6ZgxwJjC1sbalkU8+gRkz\nwiyef/wjvPgi7Lproa1yHCfuNOVNYH/gPUmPSHpV0oOSdgbam9lqADN7G9gzqt8BWJ5x/MqorAOw\nIqN8RVTWZEpKSnLRTKxZtw6eeqqEffYJA8G6dAkzfB54YKEtyz1puJ6QHp2QHq1x1tmUYMH2QE/g\nMjN7RdI9hCf+6plbz+TmCbMwm2enTrBoEey9d6Etchyn2GiKE1gBLDezV6LtxwlOYLWk9ma2WtJe\nwDvR/pVAp4zjO0Zl2cprpLS0lM6dOwPQtm1bevToscXLVsXdqraHDx9e6/44b3/wAdx0UzlvvQWn\nn17CrrvCnDnlfPghLF1aQps28N575axeDddfD3vvXRIr+/OxXczXsyHbVWVxsSef23PmzGHw4MGx\nsSdf29WvbXOcr6ysDGDL/TIbTeoiKukvwA/NbLGkYcDO0a61ZnabpGuAdmY2JEoMjwOOIoR7ngW6\nmJlJmglcCcwGngJGmNkzNZyvQV1Ey8vLt/xAxUBlJdxxB9x8c5i2+ayzwlQP8+bB+vVhzd6dd4be\nvcM8/g8+GPIAy5YVl87GUmzXs7GkRSekR2uhdeZt2ghJRwC/BXYAlgI/AFoAEwhP9xVAPzN7P6o/\nFBgAbAQGmdm0qPxIoAxoRehtNCjL+RI3TmDTphDDf/rprUszjhsHnTv7XD2O4+QGnzsopqxeDaed\nBhs2hIFdvXvDsccmq1un4ziFx+cOiiFvvBHm6O/bF+bOhdtvhxNOaJwDiLPOXOI6k0datMZZZ6Kd\nQBzZvDn04z/2WPj5z+GGGzzs4zhO4fBwUDOwahW8/XZY0vH++0NXzl/+Mjz5O47j5JvawkEefc4T\nb70FkyaFz5w5sN9+YVnHSZPgyCMLbZ3jOE4g0eGg5o7DzZ8fZuz83vfCDf/ll8PyjMuXw6uvwtix\n+XEAcY435hLXmTzSojXOOv1NIEe88kpYiP2ss+Ckk2DUKJ+/33Gc+OM5gRzwn//A0UfDyJHwrW8V\n2hrHcZxtSW0X0eZg3To49VS47jp3AI7jFB+JdgL5jsO99VYI/Zx2WljEpVDEOd6YS1xn8kiL1jjr\nTLQTyCdPPQW9eoUcwB13FNoax3GcxuE5gUYwYgTcdlvo7XP88YW2xnEcp3Z8nEAOMINZs6CsDP7y\nl7B8Yx0ztDqO48SeRIeDchGHq6yE8ePhy1+GCy6Ali3j5wDiHG/MJa4zeaRFa5x1+ptALbz4Ilx6\nKey4I9xyS5jlc7tEu03HcdKG5wRq4J134N57w4Cv++6D737XJ3lzHKd48XEC9eTtt+Hqq+Hgg4Mj\neOWVMAWEOwDHcZJKk52ApO0kvSppcrTdTtI0SW9Imippt4y6QyUtkbRQ0skZ5T0lzZW0WNLwptpU\nRX3jcKtWweDB0L17WOlr3jx46CHo2DFXluSXOMcbc4nrTB5p0Rpnnbl4ExgELMjYHgI8Z2YHAdOB\noQDRGsP9gG5AH+ABacsz9khggJl1BbpK6p0Du7LyyScweTL86EdhUZdDDw1P+/PnhzBQhw75PLvj\nOE58aOoawx2BR4BfAleZWV9Ji4BjzWy1pL2AcjM7WNIQwMzstujYp4EbCesQTzez7lF5/+j4S2s4\nX5NyAv/+d3jiLy8PA71OPx323TfM+7PXXo1u1nEcJ9bkc5zAPcBPgd0yytqb2WoAM3tb0p5ReQfg\npYx6K6OyTcCKjPIVUXlOGTsWrroKhgwJC7n7DJ+O4zhNCAdJOg1YbWZzgNpSpwXrflReXs7ChXDG\nGfCrX8Gzz4bEb9IcQJzjjbnEdSaPtGiNs86mvAkcDfSVdCqwE9Ba0ljgbUntM8JB70T1VwKdMo7v\nGJVlK6+R0tJSOkcjtdq2bUubNj3Yb78S9t0Xpkwp54AD4IwzSvjoI/jlL+cwaxYMG1bCxInw0kvl\nlJdDSUkJsPXCFPt2FXGxJ1/bc+bMiZU9fj2bvj1nzpxY2ZOU7fLycsrKygC23C+zkZNxApKOBa6O\ncgK3A2vM7DZJ1wDtzGxIlBgeBxxFCPc8C3QxM5M0E7gSmA08BYwws2dqOI898YRRWQmtW4fVuu68\nM3TpXL4c2reHxYvhqKNg5kzo0wfuuius6es4jpNWmnvuoFuBCZIuIiR9+wGY2QJJEwg9iTYCAzOy\nvJcBZUArYEpNDqCKX/widN386CPYZZdwsz/ggK37V60K0zqUlQWn4DiO42Sn6EYMb9pktGhRv/rl\n5eVbXpWSjOtMFmnRCenRWmidiRoxXF8H4DiO49RN0b0JFJO9juM4cSBRbwKO4zhO7ki0E6je5S6p\nuM5kkRadkB6tcdaZaCfgOI7j1I7nBBzHcRKO5wQcx3GcGkm0E4hzHC6XuM5kkRadkB6tcdaZaCfg\nOI7j1I7nBBzHcRKO5wQcx3GcGkm0E4hzHC6XuM5kkRadkB6tcdaZaCfgOI7j1I7nBBzHcRKO5wQc\nx3GcGmnKGsMdJU2XNF/SPElXRuXtJE2T9IakqZJ2yzhmqKQlkhZKOjmjvKekuZIWSxreNElbiXMc\nLpe4zmSRFp2QHq1x1tmUN4FNwFVmdgjwdeAySQcDQ4DnzOwgYDowFCBaXrIf0A3oAzwgqer1ZCQw\nwMy6Al0l9W6CXVuoWpM26bjOZJEWnZAerXHW2WgnYGZvm9mc6PvHwELCIvHfAkZH1UYDZ0bf+wLj\nzWyTmS0DlgC9osXoW5vZ7KjemIxjmsT777+fi2Zij+tMFmnRCenRGmedOckJSOoM9ABmAu3NbDUE\nRwHsGVXrACzPOGxlVNYBWJFRviIqcxzHcfJMk52ApF2BicCg6I2gevedgnXnWbZsWaFO3ay4zmSR\nFp2QHq2x1mlmjf4A2wPPEBxAVdlCwtsAwF7Awuj7EOCajHrPAEdl1onK+wMjs5zP/OMf//jHPw3/\nZLuPb0/TeBhYYGb3ZpRNBkqB24ALgSczysdJuocQ7jkQmGVmJukDSb2A2cAFwIiaTpatn6vjOI7T\nOBo9WEzS0cBfgXls9TbXArOACUAnoALoZ2bvR8cMBQYAGwlvD9Oi8iOBMqAVMMXMBjVekuM4jlNf\nimrEsOM4jpNbfMSw4zhOinEn4DiOk2LcCTiO46QYdwKO4xQUSTcU2obmIo5aU+EE4vjD5wPXmTxS\novXiQhvQjMROayp6B0l608z2LbQd+cZ1Jo+kaJX0YbZdwE5m1tQxS7Gh2LTGypimUNcP35y25BPX\nmSydkBqt7wNfrZpXLBNJy2uoX8wUldbEOAGK7IdvAq4zWTohHVrHAPsBn9MIPNrMtuSbotKaJCdQ\nVD98E3CdydIJKdBqZtfXsu+a5rQl3xSb1lTkBBzHKTzRIlK92DpV/Eqi+cMKZ1XzIulgM1tUaDsy\nSYwTkNQS2Fj1D0rScUBPwgR3TxfUuBwi6XAzm1toO5oDSfsCH5rZ+9GaFV8BFpnZ6wU1LE9I+gph\nzq3NwOK43SyaQrSc7AOExaRWRsUdCRNJDqyaRyzpxDHRnyQn8BpQYmbrJP0UOAuYAhwL/MPMhhTU\nwBwhaTOwFBgPPGZmCwpsUl6QNAT4EbABuBP4CfB34GvAKDO7u4Dm5RRJxwJ3EXIDRxJ0tiNMtHi+\nmRV9XkDSQqBPtKpgZvn+hEkjuxXEsDwgqcZZkAmJ/gvNrE1z2lMXSXICr5vZodH3V4BvmtlnkrYH\nXjWzwwtrYW6Q9E/gfOAc4GzgE+AxwtKdywpoWk6RNJ/w5L8zsAw4wMzelbQL8HLVtU4C0TU9OdK3\nP3C3mZ0l6STgp2Z2coFNbDKSlgDdzGxTtfKWhLf1AwtjWe6R9BFwNeEBpjp3mdkXmtmkWklSYvhD\nSYdGoYL3CNNSf0bQmKRBcRZpvA64LlqHoT/wQvSq+Y3CmpczNkdO/L+E67gGwMw+CaHlRNHCzN6N\nvr9JSBJjZs9KGl44s3LKw8BsSePZusxsJ8K/3VEFsyo/zAZeN7MXq++QdGPzm1M7SXoTOBwYC7wW\nFVWtd3AY4ckqEb0sJP3TzL5cQ7mAY8zsLwUwK+dIKgNaArsAnwKbCKvRHQ+0NrN+hbMut0h6mLAe\nx3SgL7DSzK6StDPhLfbgghqYIyR1J+jLTAxPTlpIU9LuwHoz+7TQttSHxDgBAEktgJOBroQ3gBXA\n1KpFbZKApHOT4tBqIwrjfY9wc5xIWIr0HMKT8v+Z2ScFNC+nSNoB+CHQnfAQ87CZbZa0E7CnmVUU\n1EAn0STKCTiOE08k7QYMBc4E9iQ493cIy8/emrAHtaLSmphYuaRdJd0kaX60ZvG7kmZKKi20bbnE\nderCQtuWazK0vp7gazoBWEfowbe7me0BHBeVTSioZbmnqLQm5k1A0pPAH4HngH6EWPJ44HpCjPXa\nApqXM1xnsnRCOrRKesPMDmrovmKk2LQmyQm8ZmZHZGzPNrOvStqO0AUtKck115kgnZAOrZKmEZzc\n6Ko5kiS1B0qBk8zsxAKal1OKTWtiwkHAJ5L+B0BSX2AtgJlVEgZpJAXXmSydkA6tZwN7AH+RtE7S\nWqAc2J3w9pMkikurmSXiAxwOzCLE3V4AukblXwSuLLR9rtN1pl0rcDBwIrBrtfJTCm1bmrUmJhxU\nG5J+YGaPFNqOfOM6k0dStEq6ErgMWAj0AAaZ2ZPRvlfNrGch7cslxaY1LU4gdpM25QPXmTySolXS\nPODrZvZxNBngRGCsmd2bbQBksVJsWhMzbYSkbDNrCmjfnLbkE9eZLJ2QGq3bmdnHAGa2TFIJMFHS\nfiQn71FFUWlNjBMg/GfpTYirZiLgc3N4FDGuM1k6IR1aV0vqYWZzAKKn5NMJcwodVljTck5RaU2S\nE/gzIQkzp/oOSeXNb07ecJ3J0gnp0HoBYf6nLViYUfQCSb8pjEl5o6i0piIn4DiO49RMksYJOI7j\nOA3EnYDjOE6KcSfgOI6TYtwJOI7jpBh3Ao7jOCnm/wMQf2cQlFKqcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa97353ec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Real GDP: showing rise from $4 trillion economy\n",
    "#  in the 1960's to nearly $19 trillion dollars.\n",
    "plot( gdpr )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.82, 2.83, 1.07, 12, 691, '1959-01-01', '2016-07-01']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "georet( gdpr, 12 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Real GDP geometric rate of growth is 2.8% per annum \n",
    "(presumably due to the working population). \n",
    "We could say that is the *natural growth rate* \n",
    "of the US economy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Real GDP per worker (NOT per capita)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Real GDP per worker -- NOT per capita:\n",
    "gdprworker = todf( gdpr / workers )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEaCAYAAAABnax5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa96afa0c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot( gdprworker )\n",
    "#  plotted in thousands of dollars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Chart shows each worker on average currently contributes \n",
    "over *90,000 dollars annually* \n",
    "of goods and services valued as GDP.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.64, 1.65, 1.36, 12, 691, '1959-01-01', '2016-07-01']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "georet( gdprworker, 12 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Workers have generally been more *productive* since WW2, \n",
    "increasingly contributing to GDP at an annual pace of 1.6%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine wage income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Nominal annual INCOME, assuming 40 working hours per week, 50 weeks per year:\n",
    "inc = get( m4wage ) * 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  REAL income in thousands per worker:\n",
    "rinc = todf((defl * inc) / 1000.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEaCAYAAADaJgZbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa974b50c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Income in thousands, current dollars per worker:\n",
    "plot( rinc )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**INCOME chart shows each worker on average currently earns \n",
    "about *43,300 dollars annually* \n",
    "(steadily up from 35,000 since the 1990's).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                    Y\n",
       "T                    \n",
       "2016-03-01  43.256085\n",
       "2016-04-01  43.255503\n",
       "2016-05-01  43.214448\n",
       "2016-06-01  43.233339\n",
       "2016-07-01  43.347900\n",
       "2016-08-01  43.318762\n",
       "2016-09-01  43.360000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tail( rinc )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.48, 0.48, 0.82, 12, 633, '1964-01-01', '2016-09-01']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "georet( rinc, 12 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, real income does not always steadily go up, \n",
    "as the chart demonstrates. A stagnating economy with high inflation \n",
    "will wear away real wages.\n",
    "\n",
    "Since 1964, the geometric rate of real wage growth has been \n",
    "approximately 0.5% -- far less in comparison to the \n",
    "natural growth rate of the economy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How do wages multiply out to GDP?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Ratio of real GDP to real income per worker:\n",
    "gdpinc = todf( gdprworker / rinc )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implicitly our assumption is that workers earn wages at the \n",
    "nonfarm non-supervisory private-sector rate. \n",
    "This is not a bad assumption for our purposes, \n",
    "provided changes in labor rates are uniformly applied \n",
    "across other various categories since we are \n",
    "focusing on the multiplier effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEaCAYAAADntGc9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni7hzzoWYF3HnnAux/w83EVR+qPiV+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa96e88cc>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot( gdpinc )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                   Y\n",
       "T                   \n",
       "2016-01-01  2.226120\n",
       "2016-02-01  2.222326\n",
       "2016-03-01  2.219059\n",
       "2016-04-01  2.225794\n",
       "2016-05-01  2.230470\n",
       "2016-06-01  2.236654\n",
       "2016-07-01  2.232221"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tail( gdpinc )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The ratio of real GDP to real income per worker has increased \n",
    "from 1.4 in the 1970's to 2.2 recently.* \n",
    "(There is a noticeable temporary dip after the 2007 crisis.) \n",
    "\n",
    "**One dollar in paid wages currently \n",
    "yields 2.23 dollars of products and services.**  \n",
    "\n",
    "The time-series shows workers have become \n",
    "more productive in producing national wealth.\n",
    "\n",
    "Hypothesis: over the years, *technology* has exerted \n",
    "upward pressure on productivity, and downward pressure on wages. \n",
    "In other words, the slope of gdpinc is a function of \n",
    "technological advances. \n",
    "(Look for counterexamples in other countries.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  regresstime slope = 0.00171483954109\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEaCAYAAADntGc9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9ac2aec>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Let's fit and plot the simplified time trend:\n",
    "gdpinc_trend = trend( gdpinc )\n",
    "#  The computed slope will be relative to one month.\n",
    "\n",
    "plot( gdpinc_trend )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The estimated slope implies that each year adds 0.02 to gdpinc multiplier.\n",
    "\n",
    "Clearly, we can rule out a *constant* gdpinc multiplier effect.\n",
    "\n",
    "Rather than a straight line estimator, we can forecast \n",
    "the gdpinc multiplier using the Holt-Winters method, \n",
    "one year ahead, month-by-month..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Forecast\n",
       "0   2.232221\n",
       "1   2.222580\n",
       "2   2.220730\n",
       "3   2.218881\n",
       "4   2.217031\n",
       "5   2.215182\n",
       "6   2.213332\n",
       "7   2.211483\n",
       "8   2.209633\n",
       "9   2.207784\n",
       "10  2.205934\n",
       "11  2.204085\n",
       "12  2.202235"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Holt-Winters monthly forecasts:\n",
    "holtfred( gdpinc, 12 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, forecasting the local terrain is more complex \n",
    "than a global linear regression. \n",
    "The Holt-Winters forecast for mid-2017 shows a \n",
    "0.03 *decrease* in the gdpinc multiplier.\n",
    "\n",
    "If gdpinc multiplier is constant, then mathematically a \n",
    "x% change in wages would translate to a straightforward x% change in GDP. \n",
    "This is why the Fed Reserve, especially Janet Yellen, \n",
    "pays so much attention to wage growth. \n",
    "But our analysis clearly shows the multiplier is not stable.\n",
    "\n",
    "Linear regression between real GDP growth and real wage growth \n",
    "performs poorly when the multiplier is treated \n",
    "as if it is time-invariant (see Appendix 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONCLUSION: Numerical approximation for GDP growth based on observations from wage growth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found evidence of a **time-variant multiplier** \n",
    "$m_t$ such that $G_t = m_t w_t$. \n",
    "Let us express GDP growth as the percentage change:\n",
    "\n",
    "$\\begin{aligned}\n",
    "\\frac{G_{t+1} - G_t}{G_t} = \\frac{m_{t+1} w_{t+1}}{m_t w_t} - 1\n",
    "\\end{aligned}$\n",
    "\n",
    "Notice that LHS is just the growth rate of $m_t w_t$. \n",
    "Abusing notation, we could write $\\%(G) = \\%(m w)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Empirically the multiplier varies linearly as a function of time. \n",
    "\n",
    "Let us evaluate the GDP growth numerically on the LHS, \n",
    "using the most recent multiplier and its expected linear incrementation, \n",
    "assuming wage increase of 1% *year-over-year*:\n",
    "\n",
    "$\\begin{aligned}\n",
    "(\\frac{2.23 + 0.02}{2.23}) {1.01} - 1 = 0.0191\n",
    "\\end{aligned}$\n",
    "\n",
    "Under such assumptions, GDP increases 1.91% over one year.\n",
    "\n",
    "In other words, **as a rough current approximation: \n",
    "GDP_growth = 1.9 \\* wage_growth,** i.e.\n",
    "\n",
    "$\\%(G) \\approx 1.9 * \\%(w)$ at current estimated parameters.\n",
    "\n",
    "This is an useful approximation since GDP is only released quarterly, \n",
    "whereas wage data is released monthly. \n",
    "(The result also depends on the interpolation\n",
    "method used in our *resample_main()*.)\n",
    "\n",
    "Appendix 3 arrives at the following linear regression result:\n",
    "\n",
    "$\\%(G) \\approx 1.3 * \\%(m w)$\n",
    "\n",
    "which takes the entire dataset since 1964 into account, \n",
    "using gdpinc_trend as time-varying multipler."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - - -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### APPENDIX 1: Linear regression of 0.49 R-squared if gdpinc multiplier is mistakenly treated as a constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  FIRST variable:\n",
      "count    691.000000\n",
      "mean      66.485116\n",
      "std       16.522582\n",
      "min       37.386155\n",
      "25%       55.670744\n",
      "50%       65.051719\n",
      "75%       79.089637\n",
      "max       96.832448\n",
      "Name: Y, dtype: float64\n",
      "\n",
      " ::  SECOND variable:\n",
      "count    633.000000\n",
      "mean      38.031736\n",
      "std        2.434287\n",
      "min       33.713467\n",
      "25%       35.911289\n",
      "50%       37.667098\n",
      "75%       39.439192\n",
      "max       43.360000\n",
      "Name: Y, dtype: float64\n",
      "\n",
      " ::  CORRELATION\n",
      "0.70095718007\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      Y   R-squared:                       0.491\n",
      "Model:                            OLS   Adj. R-squared:                  0.491\n",
      "Method:                 Least Squares   F-statistic:                     607.6\n",
      "Date:                Thu, 10 Nov 2016   Prob (F-statistic):           2.12e-94\n",
      "Time:                        18:48:39   Log-Likelihood:                -2386.8\n",
      "No. Observations:                 631   AIC:                             4778.\n",
      "Df Residuals:                     629   BIC:                             4786.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    -95.1989      6.677    -14.257      0.000      -108.311   -82.087\n",
      "X              4.3208      0.175     24.649      0.000         3.977     4.665\n",
      "==============================================================================\n",
      "Omnibus:                     1052.387   Durbin-Watson:                   0.001\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               68.013\n",
      "Skew:                          -0.477   Prob(JB):                     1.70e-15\n",
      "Kurtosis:                       1.705   Cond. No.                         601.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "stat2( gdprworker[Y], rinc[Y] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - - -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### APPENDIX 2: Linear regression between real GDP growth and real wage growth: 0.19 R-squared when multiplier is treated as time-invariant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: an alternative is to use the difference betweeen \n",
    "logarithmic values, but we intentionally use the pcent() function YoY \n",
    "since our data frequency is not even remotely continuous-time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ::  FIRST variable:\n",
      "count    679.000000\n",
      "mean       2.884252\n",
      "std        2.437540\n",
      "min       -3.832416\n",
      "25%        1.656733\n",
      "50%        3.006804\n",
      "75%        4.260688\n",
      "max        9.206530\n",
      "Name: Y, dtype: float64\n",
      "\n",
      " ::  SECOND variable:\n",
      "count    621.000000\n",
      "mean       0.471273\n",
      "std        1.359933\n",
      "min       -3.716831\n",
      "25%       -0.508176\n",
      "50%        0.580607\n",
      "75%        1.355072\n",
      "max        4.590047\n",
      "Name: Y, dtype: float64\n",
      "\n",
      " ::  CORRELATION\n",
      "0.43615914959\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      Y   R-squared:                       0.190\n",
      "Model:                            OLS   Adj. R-squared:                  0.189\n",
      "Method:                 Least Squares   F-statistic:                     144.9\n",
      "Date:                Thu, 10 Nov 2016   Prob (F-statistic):           3.92e-30\n",
      "Time:                        18:48:39   Log-Likelihood:                -1361.0\n",
      "No. Observations:                 619   AIC:                             2726.\n",
      "Df Residuals:                     617   BIC:                             2735.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      2.3898      0.093     25.728      0.000         2.207     2.572\n",
      "X              0.7768      0.065     12.039      0.000         0.650     0.904\n",
      "==============================================================================\n",
      "Omnibus:                       54.393   Durbin-Watson:                   0.035\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               98.036\n",
      "Skew:                          -0.568   Prob(JB):                     5.15e-22\n",
      "Kurtosis:                       4.584   Cond. No.                         1.65\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#  Examine year-over-year percentage growth:\n",
    "stat2( pcent(gdpr, 12)[Y], pcent(rinc, 12)[Y] ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - - -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appendix 3: Improved linear regression of growth model: 0.60 R-squared with time-variant multiplier (trend based)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let the Python variable mw represents the series $m_t w_t$ \n",
    "in our analytical model described in the conclusion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  The string argument allows us to label a DataFrame column:\n",
    "mw     = todf( gdpinc_trend * rinc, 'mw' )\n",
    "mwpc   = todf( pcent( mw, 12), 'mwpc' )\n",
    "gdprpc = todf( pcent( gdpr, 12), 'Gpc' )\n",
    "dataf = paste( [gdprpc, mwpc] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    Gpc   R-squared:                       0.602\n",
      "Model:                            OLS   Adj. R-squared:                  0.601\n",
      "Method:                 Least Squares   F-statistic:                     933.7\n",
      "Date:                Thu, 10 Nov 2016   Prob (F-statistic):          1.17e-125\n",
      "Time:                        18:48:40   Log-Likelihood:                -1398.1\n",
      "No. Observations:                 619   AIC:                             2798.\n",
      "Df Residuals:                     618   BIC:                             2803.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "mwpc           1.3131      0.043     30.557      0.000         1.229     1.397\n",
      "==============================================================================\n",
      "Omnibus:                       85.296   Durbin-Watson:                   0.044\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              202.796\n",
      "Skew:                          -0.736   Prob(JB):                     9.19e-45\n",
      "Kurtosis:                       5.387   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#  The 0 in the formula means no intercept:\n",
    "result = regressformula( dataf['1964':], 'Gpc ~ 0 + mwpc' )\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R-squared after 1964 looks respectable at around 0.60, however, \n",
    "the fit is terrible after the Great Recession. \n",
    "\n",
    "The estimated coefficent implies this fitted equation: \n",
    "\n",
    "$\\%(G) \\approx 1.3 * \\%(m w)$\n",
    "\n",
    "In contrast, our *local numerical approximation* derived in the conclusion \n",
    "suggests for the most recent estimated parameters: \n",
    "\n",
    "$\\%(G) \\approx 1.9 * \\%(w)$"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}